Analysis of Interpretive Structural Modeling (ISM) of Collaboration Drivers in the Supply Chain of SAIPA Automotive Group

The purpose of this study is to identify the critical drivers of collaboration within the SAIPA Group’s supply chain to enhance and strengthen cooperation through effective management practices. The study employs a quantitative and applied research methodology. The statistical population consists of academic experts with at least 10 years of professional experience, selected through a judgmental sampling method, with a total sample size of 10 individuals. Data collection was conducted using a researcher-designed questionnaire focusing on collaboration drivers within the supply chain. The data were analyzed using the Interpretive Structural Modeling (ISM) method to identify and evaluate the relationships between the drivers. The findings indicate that environmental characteristics, including factors such as uncertainty and competitive pressures, serve as an independent variable influencing all other factors in the supply chain. The study highlights that effectively managing this variable requires organizations to recognize internal capabilities, optimize performance, and translate future scenarios into actionable steps. These measures will significantly contribute to improving collaboration within the supply chain. This study emphasizes the importance of collaboration in overcoming challenges such as limited resource access, complementary capabilities, and partner expertise. The results underline the need for structured collaboration to drive innovation, organizational learning, and enhanced performance, offering new insights into managing collaboration in automotive supply chains.